The wealth management landscape in the GCC region is evolving rapidly. A Compound Annual Growth Rate (CAGR) of 6.3% is expected between 2024 and 2028. The use of artificial intelligence tools (AI) in wealth management is projected to grow at a CAGR of 25.5% until 2028. This indicates a significant shift towards technology adoption among wealth managers in the GCC, driven by the need for enhanced efficiency, personalised services, and improved client experiences.
“The fast adoption of new technologies is also accelerated by lower legacy in technology compared to more established wealth management hubs,” said Radomir Mastalerz, CEO of WealthArc. “The GCC has commenced its journey towards AI adoption and appears to have all the ingredients to become one of the leading regions in the world in adopting AI-driven solutions.”
AI can provide speed and accessibility to information that was unseen before. It has one significant advantage–AI can process and synthesise much more information than a human can. Analysing news and financial statements, discovering sentiments on social media, synthesising market analysis and finding trends in the market are all tasks that can help improve investment strategies and allow one to react fast to risky market events.
Data from McKinsey & Company shows that 75% of clients expect their wealth managers to use digital tools and AI to provide personalised services. “Nevertheless, wealth managers must balance the benefits of AI-driven client engagement with ethical considerations and robust security measures,” explained Mastalerz. “AI services are now being tested and evaluated by wealth management professionals.”
He stressed that risks such as data privacy concerns and algorithmic bias must be mitigated before AI is exposed to investors. Once the risks are controlled, AI services will be exposed to asset owners.
When asked about opportunities for enhancing operational efficiency using AI-driven solutions, Mastalerz stated, “by automating routine tasks, streamlining processes and providing faster access to synthetised information, AI technologies enable wealth managers to focus on value-added activities.”
Let us consider a few examples. A wealth manager can prepare personalised investment advice, which is time-consuming and unscalable. Wealth managers can find relevant investment information, but AI models can help them find the information faster. They can analyse news, but AI will do it faster and more efficiently. Wealth managers can track changes in regulatory compliance, but AI can do it automatically. As a result of increased operational efficiency, wealth management democratisation will progress.
There are at least three objectives for family offices and wealth managers who are looking to implement AI-powered solutions:
- Attract new clients, including the affluent segment
- Educe cost by increasing operational efficiency
- Drive wealth creation by making smarter investment decisions
“In the long run, AI will facilitate the democratisation of wealth management,” said Mastalerz. Wealth management services will be cheaper, more accessible, and more personalised.”
Neuro-symbolic AI technologies such as KGQA and LLMs address the limitations of traditional wealth management tools by combining symbolic reasoning with neural network-based learning approaches. According to a study by Accenture, 76% of wealth management executives believe that AI technologies improve data analysis accuracy and decision-making capabilities. LLMs facilitate an easy interface for interacting with data using natural language. KGQA ensures the meaning, accuracy, and consistency of the returned answers.
Key challenges
Data accuracy is the biggest and most important challenge wealth managers face while incorporating AI tools. Accurate and complete data is the foundation of any AI technology. Portfolio management in GCC is characterised by a larger allocation to alternative assets compared to, for example, the European market. Collecting data on liquid and alternative assets is cumbersome, and accuracy may be questionable. Without a solid data foundation, the implementation of AI is destined to fail.
Mastalerz said that the neuro-symbolic AI can help automate portfolio management and reporting. “Neuro-symbolic AI systems seek to integrate neural network models (like LLMs) with symbolic knowledge-based approaches,” he explained. “Symbolic AI is based on manipulating symbols and rules to represent knowledge and perform logical reasoning.”
“It is good at tasks that require explicit, rule-based and structured representation of information.”
Neural networks, conversely, are machine-learning models inspired by the human brain. They excel at image and speech recognition, natural language processing and other pattern recognition tasks.
WealthArc, a Swiss-born global wealth data management solution provider, partnered with ZeroLink to develop a machine learning-powered ‘Chat with your data’ service. The company hopes to target family offices and the wealth management market across the GCC, which has Assets under Management (AuM) worth $650 billion.
The new AI and data analytics solution, the firm said, combines the strengths of neural networks and symbolic logic using neuro-symbolic KGQA (knowledge graph question/answering) and LLM (large language models) to create access to up-to-date and accurate intelligent information that enables family offices and wealth managers to access, navigate, comprehend, and interact with complex data sets.
Regulatory considerations and compliance requirements
Mastalerz stressed that AI regulation in financial services is required to ensure data security, privacy protection, and ethical use of AI. Introducing regulations in a completely new area is always challenging. Regulatory frameworks cannot be created in isolation. “A practical, hands-on approach is required to understand AI and its impact on society,” he said.
OpenAI CEO Sam Altman believes the UAE could become a ‘regulatory sandbox’ for artificial intelligence on the global stage. During the World Governments Summit, he encouraged the UAE to act as a testing ground for AI technologies and help shape global regulatory frameworks.
Future trends
When asked about the trends for the next ten years, Mastalerz believed that an emerging trend would be the popularisation of hybrid financial advice. Financial firms will maintain a balance between AI and human advice. While AI can automate routine tasks and offer data-driven insights, it should be viewed as a partial replacement for human advisors. The second trend will be the creation of regulatory sandboxes to test AI solutions. Countries will compete for faster but controlled introduction of AI in financial services.